#402 ‒ NMR blood analysis: how heart disease risk, insulin resistance, inflammation, and mortality risk can be assessed from a single blood sample | Jim Otvos, Ph.D.

26:31Peter Attia:One point to just add to that, Jim, that a person who's looking at their own NMR result in their blood test will notice that the units are reported as nanomoles per liter as opposed to milligrams per deciliter. So it's not a mass concentration, it's a molar concentration. Maybe explain to people what that distinction is. Yeah, that's just where I was going to go next. Because as I told you, the chemical constituents in these particles, and they're quite heterogeneous, there's different size legs of fatty acid chains. Some are saturated, some are monounsaturated, some are polyunsaturated. All of these things give rise to the lipid complexity of these particles.

27:11But the NMR detection, at least of this signal, knows nothing about any of that. It's basically a lipoprotein particle signal. And so what should be the case, if that's true, is that how big that NMR signal is from the particle should relate to the number of particles, irrespective of what the lipid concentrations are. And there are variable amounts of cholesterol and triglyceride in most lipoproteins. So what we realized at the beginning was if this was a lipoprotein particle signal we were interrogating, we could get lipoprotein particle concentration information, but we could not and should not report lipoprotein cholesterol levels or lipoprotein triglyceride levels because we actually weren't able to differentiate the signals from those different chemical species.

28:06So that is what we started to produce when we did this commercialization thing, which I may come back to a little bit. But as you said, what's reported LDLP is the particle concentration in nanomoles per liter. So there's 6.02 times 10 to the 23rd particles in a mole of LDL. And so this is 10 to the minus ninth. Anyway, so it's a big number. It's still 10 to the 16th particles. So there's a lot of these particles in your blood, but you're reporting the concentration of the package, not the lipid molecules in the package. So then the question is, or one question is, is there any advantage to that?

28:53I mean, because by then, so I'll go back a little bit to the commercialization, because as I said, the commercialization was driven by the idea that small dense LDL is much more atherogenic, much more to worry about than large LDL. And our own studies, when we started measuring small and large LDL by NMR and did it in large population studies, we found exactly the same thing that Ron Krauss and others did. And which population did you look at? First, Mesa or Framingham? Framingham, for sure. Back in the day, Mesa came a little bit later. But I can't remember what we initially looked at. But the point is that when looked at through the lens of at a given quantity of LDL, if the LDL is small versus large, does it make a difference in your cardiovascular risk?

29:44When I said the concentration of the quantity of LDL, the way everybody thinks of the concentration of LDL is LDL cholesterol. And that's what Ron Kraus and that's what we did. We said, okay, at a given level of LDL cholesterol, you take people and you stratify them according to low, medium, and high LDL cholesterol, or you do a multilinear regression and put LDL cholesterol in the model, and now you're asking, does the size of the LDL add anything to LDL cholesterol in cardiovascular risk prediction? And sure enough, it does, and it's quite an impressive increment of risk. So we reproduced what Ron Cross did.

30:21However, we realized because we were also in the business of measuring LDL particles that by definition, if an LDL particle is smaller versus larger, it's always full of lipid. You don't get a partially filled container of LDL. It's always full. So a smaller LDL particle means by definition that it's carrying less cholesterol, less lipid per particle than a large LDL particle. So the phenomenology is at a given level of LDL cholesterol, people with small, dense LDL have higher cardiovascular risk. The trouble is that people with small, dense LDL at a given level of LDL cholesterol have more LDL particles.

31:02Their LDL-P is higher than would have been imagined from the LDL cholesterol measurement. So an alternate explanation for the extra risk that small dense LDL seem to confer is that there are simply more particles rather than the size of the particles being the determining characteristic of the atherogenicity of these particles.

31:25Peter Attia:Yeah. Let me just use a silly childlike example to make this point. So imagine you had four lipoproteins that each contain three units of cholesterol. They're fully saturated at three units of cholesterol. So you have four of them. So you have 12 units of cholesterol. Now imagine you have three spherical lipoproteins that each have four units of cholesterol. They're fully saturated, so they're obviously bigger, but they also collectively have 12 units of cholesterol. So here you have two people. One has 12 units of cholesterol, but it's being carried with four lipoproteins. The other has the same 12 units of cholesterol carried by three.

32:04Peter Attia:You're telling me all the data say the first person is at higher risk. The question is, are they at higher risk because their spheres are smaller, or are they at higher risk because they have more spheres, which happen to be smaller? Correct. So you explained that very well. So this is basically how science works. So you have a different explanation for the phenomenology of small dense LDL having this seemingly extra atherogenicity. So it's then testable. So it's quite straightforward at that point to ask the question at a given level of LDL particles, if you stratify people according to LDL-P, and then say some people have small LDL, some people have large, is there any difference in their risk?

32:51And the answer is not, not a bit.

32:53Peter Attia:So now again, now the example is you have two patients that each have 20 particles. One of them is 10 big, 10 small. The other one is 15 big, 5 small. If the particle size matters, the first one should be at higher risk. If the particle size is irrelevant, once you've corrected for total number, they should be at the same risk. You're saying they're at the same risk. That's right. And why does this matter? I mean, if you're only interested in assessing the risk of a person, you're equally well off with the cholesterol information and the size information as the particle information and the size of the particle information.

33:34The size doesn't add to that. The reason that's important is that if you believe that small LDL is bad and you can make it less bad by making the particles bigger therapeutically somehow, then you will be telling patients that at a given, let's say they get treated with statins and they lower their LDL cholesterol or their LDL particle concentration to an acceptably low level, but the particles are still small, somebody who believes small dense LDL is particularly bad will then try to do something to make them bigger and imagine that there's clinical benefit. And a lot of drugs actually have that effect.

34:17CETP inhibitors are one of them. Niacin, HDL drugs of different types. So triglyceride lowering automatically will have this effect. So there really are a lot of clinical trials that imagined when they set out to do the clinical trial that there would be a lot of efficacy, like niacin, for example, because not only did that modestly raise HDL cholesterol and lower triglyceride, which were two good things, seemingly. But it made the LDL particles bigger, and it made the HDL particles bigger, because there's also a similar argument about the size being important in HDL. And yet there was no efficacy when they did the outcome studies.

35:03And this has been reproduced with the fibrates and other drugs.

35:09Peter Attia:So, Jim, what would you say to the person listening who, because I hear this all the time, who says, hey, guys, I do have a high LDL cholesterol and even my LDL particle number is very high on my NMR test, but I'm very pattern A. You see, all of my LDL are very large and they even use words like fluffy. Yes, they do. And buoyant. I have large, fluffy, buoyant. Yes, LDL. So my LDL particle number is over 2 ,000 nanomole per liter, which probably places me at the 80th percentile or so. But I don't need to worry about it because they're all big. I don't have that pattern B. I don't have that small.

35:49Peter Attia:What would you say to that person? I would say that's a fallacious idea. And the data that I mentioned supports it completely. And then you also have to think about the fact that one of the best known or best accepted genetic reasons for cardiovascular risk is FH, familiar hypercholesterolemia. People with FH have very high LDL cholesterol levels. They also have very high LDL particle levels. But those particles are large. They're not small. And these are people that die when they're 30 or 35 years old when they're homozygous FH. So this idea that somehow fluffy, large LDL particles are not to worry about or benign is completely fallacious.

36:38Peter Attia:Let's talk about one more thing on this before we pivot away from this, which you alluded to very briefly, but we went off on a different path, which is the potential discordance that exists between LDL cholesterol and LDL particle number. In fact, when I first came to learn about your work, Jim, this was actually one of the first papers I read of yours literally 15 years ago, almost to the month. And it was in the Mesa population, and it was showing four Kaplan-Meier curves. So I'll let you explain what a Kaplan-Meier curve is, but it was basically the four scenarios, which was discordance between LDLP and LDLC when one is higher than the other each time, and then concordance between them.

37:26Peter Attia:So maybe there were three curves then. Exactly. There were three curves. Okay. So concordance between LDLC and LDLP and discordance in favor of LDLP, discordance in favor of LDLP. Okay. So that was a very, very eye-opening paper to me and I'd love you to just kind of explain that finding and what the significance are, because to this day, it's still a very important thing for clinicians and patients to understand. No, it is. And prior to that, MESA paper in 2011 was a 2007 paper. So it was the first one that we sort of introduced this idea of assessing the situation by whether the LDLP and the LDLC agreed or not.

38:06And in that case, it was a Framingham study. And in that case, one question that always comes up is, well, what defines discordance? And basically, it doesn't matter, and everybody defines it differently. And it doesn't matter because we're not saying discordance is a risk factor, right? We're simply trying to disentangle a situation where most people don't have a discrepancy between LDL-C and LDL-P. So if you're trying to understand whether LDL-P is a better measure of LDL-associated cardiovascular risk, and you do a whole study population, and 80 % of those people have concordant or in agreement LDL-C and LDL-P.

38:52Peter Attia:And just explain what that means, because people will say, well, the numbers are different. How can they be in agreement? Okay. So one way to do it, which we did in the MESA paper, is transform the milligram per deciliter cholesterol number into a percentile. So that defines people according to their rank, low, high, intermediate. And if you do the same thing to the LDLP, now you're comparing apples to apples, percentile of one, percentile of the other. And you plot that and then you see the data points all over the place. And the ones that track on the diagonal, the ones that are close to the diagonal are the ones for which a 50 percentile LDL-P is close to a 50 percentile LDL-C.

39:33So that's what we call concordance. And in that case, we picked this weird number of 12 percentile difference was our cut point, plus or minus. So everybody within 12 percentile units of each other, we called concordant. Why 12? Why not a round number? And it was because we were trying to make the concordant population, about equal in number to the discordant in one direction and the discordant in other direction population.

40:00Peter Attia:So you had 50 % in one, 25, and 50 % in the two discordant groups. No, it's actually, we're trying to make it sort of 30, 30, you know. Ah, I see. 30, 30, 30. Got it. Okay. And so that's what we did. And then we simply took these three groups of people, and you talk about Kaplan-Meier, all this is basically cumulative incidence of cardiovascular or event. So on the x-axis, you can see the number increasing. If it increases a lot, this is a higher risk subpopulation than somebody whose risk is much shallower and doesn't increase very much as a function of concentration. So that very clearly showed that for people whose LDLP and LDLC agree, you can make no argument about why LDLP is a better thing to measure than LDL-C, then it's a matter of, well, from first principles, would we expect the cholesterol to be a better measure of risk or the particle number?

40:56And really, if you're honest about it, there is no mechanistic explanation that you should really be too comfortable with for one versus the other. And we explained that in that paper, that what has happened when Ron Krauss showed that small, dense LDL was more atherogenic and that this was based on epidemiologic population studies, the question was, well, why is that? And then basically the speculation was ultimately supported by various lines of evidence that, yes, smaller LDL particles are likely to get into the arterial wall easier than a bigger particle. And then the difference in the shape of the stuff on the surface of the particles can bind more avidly to molecules that are in the arterial wall and retain it more, and then it's more readily oxidized.

41:45So all of these things, I can't tell you how many papers I've read were in the discussion section is this obligatory paragraph about why small dense LDL is so much more atherogenic. The flip side argument is that, okay, large LDL particles also get into the artery wall.

42:01Peter Attia:And when they get oxidized - They're more retained. No, no, they're not more retained. I'm just saying you could make that argument, right? Well, let's just say that they get to the end of the trail and they get taken up by macrophages and deposit their cholesterol contents into the arterial wall. Well, bigger particles have more cholesterol to deliver. More oxidative damage. So you think the more cholesterol-rich particles should be more atherogenic. But if that's counterbalanced by not getting to the end of the party as often as the small particles, maybe it's a wash. How do you answer the question?

42:34You do the study. And so that's what the discordance analyses allowed us to do. Whenever there's a discrepancy in one direction or another, the cardiovascular risk tracks with the particle number, not the cholesterol. Yeah.

42:47Peter Attia:So the curve really had a beautiful separation of those three figures. You had the concordance in the middle and then above that line, which means these are people that are dying quicker. That's when LDLP was above LDLC. And below the line, these people died much slower than you would expect. It was the flip. Cholesterol was high, but their particles were low. Exactly. Yeah. So yeah, that, and so, I mean, and I can't tell you how many debates I had to have with people. I mean, usually very well-regarded cardiologists. Tell me about it. Who just refused to acknowledge there was ever a need to look at anything beyond LDL cholesterol.

43:29Right. Well, now let me sort of go somewhere about the clinical utility of this because what people would imagine from having said all this is that LDLP should be a much better thing to use to assess cardiovascular risk. And the reality is that the way cardiovascular risk is assessed today is pretty much the same way it's always been assessed by equations that take into account total cholesterol, HDL cholesterol, diabetes, present absent, smoking, present absent, hypertension. So these are risk equations, and there's updated risk equations, just fairly recently a new one. They all have total and HDL cholesterol in it.

44:21when you ask the question, if I use LDLP or I add LDLP to a model that has those things in it, is my risk assessment better? And the answer is no, it's not better. And this sort of kills you if you're trying to make a commercial argument that you should be testing LDLP instead of LDLC for risk assessment. The reason I think that that is true is because the HDL cholesterol is in the model. And we still don't really understand whether HDL is bringing extra risk assessment to the table above and beyond what the LDL and the total cholesterol brings. Or is it the triglyceride-rich particles? Now people have swung away from the idea that HDL cholesterol is important because the HDL cholesterol-raising trials were negative or weren't positive.

45:14And then because there's an inverse correlation between triglyceride and HDL cholesterol, when HDL cholesterol is low, triglycerides are high. Oh, maybe it's the triglycerides. Well, it doesn't make sense that the triglycerides per se, the molecules triglyceride are atherogenic, but the triglyceride-rich particles that carry them could well be because they also get into the arterial wall and deliver a lot of cholesterol.

45:35Peter Attia:Now, Jim, I don't know if this was one of your papers, but I think it was. And it was around that time, probably 2012, 2013. And if I recall the paper, the figure specifically, it was a histogram, right? So on the x-axis, you had 0, 1, 2, 3, 4, 5, and you were looking at number of criteria met for metabolic syndrome. So for the listener, just to remind everybody, metabolic syndrome has these five criteria, and the more of them you have, the more likely you are to be insulin resistant. So there's one about having high blood pressure, obesity, so the truncal diameter, truncal girth, blood pressure, fasting glucose, and HDL cholesterol.

46:17Peter Attia:I think those are the five, right? It doesn't include, yeah. Okay. And what this figure showed was if you had zero of them, the probability that you were discordant between your LDL-C and LDL-P was very low. If you had one of them, the probability went up a little bit, two of them considerably, considerably three a lot. And it was a monotonic increasing relationship, Jim, such that by the time you had five out of five met sin criteria, you were virtually guaranteed to be discordant. And so I bring that up to say earlier you mentioned there's nothing wrong with being discordant per se, although you could also argue that the more discordant you are, the higher the probability that you're discordant means the higher the probability that you probably have some underlying metabolic insulin resistance.

47:09Peter Attia:Extra risk. Yeah. And so the question then becomes, is the reason that very elaborate multi-parametric risk models can erase the LDLP is that they are simply capturing all of the cardiometabolic risk indirectly and directly. Yeah. You basically just explained what I was getting at, which is because HDL cholesterol is in the standard risk assessment models, when you have low HDL cholesterol, you're likely to have more LDL-P than the LDL-cholesterol indicates or suggests. And so your risk is higher because you have higher LDL-P, not because HDL cholesterol is low. But you're not doing better in risk assessment because the HDL-cholesterol, for the wrong reason, if you will, not a causal reason, but an association reason, is telling you the same thing.

48:01So this was really important to liposcience back in the day because, and this is something that I still argue with, but Alan Snyderman and I will get to the question of ApoB. But he insists, he keeps wanting to convince people that ApoB is important for risk assessment. And what we pivoted to in light of the evidence that we found ourselves convinced people that LDLP was a better thing to measure for risk assessment to a better thing to measure for risk management. Because what you do when you have high risk is you manage it by lowering your LDL cholesterol. This is the best tool we have in the toolbox.

48:50There are not actually very and many others. We have statins, and then we have things that are doing the same job as statins, but better. So we can lower the heck out of LDL. And the question for a patient with high cardiovascular risk is, how much LDL lowering do I need? So what you really would like is a biomarker that tells you my LDL-associated risk is adequately controlled. I've gotten my LDL low enough. If it's not, I should add, if I'm on high-dose statin and I'm not there, then I want to add a PCSK9 inhibitor or something else to lower it even more. So you want the best objective measure of LDL-related risk, not total risk, but LDL-related risk.

49:36In that case, it's very clear that LDL-P or ApoB is a better biomarker because a lot of people who achieve very low LDL cholesterol have not achieved equally very low LDL particles or ApoB, and they therefore, with visibility to that, would be candidates for more aggressive LDL lowering.

50:00Peter Attia:And so we absolutely notice that as clear as day in our practice, Jim, because we are very aggressive at managing these things. Is there a biologic reason for why the discordance really happens at low levels in that way, or is it a chemical assay? Is it a property of the assays that's causing that? No, it's like everything that you ask. It's complicated. You can get into the weeds. But what's true is that the lower your LDL level is, the more likely that the cholesterol in the LDL particle is replaced to some extent by triglyceride. Because there's always this interchange, this swapping of cholesterol, ester, and triglyceride in the core of the particle.

50:42and it's driven by the relative amounts of the triglyceride-rich particles and the LDL particles, which are cholesterol-rich. The triglyceride-rich particles, the bigger that gap is, put triglyceride into LDL in exchange for cholesterol ester. And so as you lower LDL with statins or whatever, the LDL particles are lower, but the LDL cholesterol is even lower because not only has the particle number gone down, but the cholesterol in the particles independently is going down.

51:12Peter Attia:That's actually a great explanation. I did not know that. And that makes sense because we see that as clear as day. Okay. One other thing I want to talk about on this front before we pivot is, at least to my knowledge, the first composite score that you then developed out of that, which was the LPIR score. Was that indeed sort of your first foray into pool composite scores? Yes. And it was still used to this day. So many people again, listening to us will have an LPIR score every time they get their blood test. No, it's interesting because, as I said, we first got into this game because we could measure small LDL.

51:47And so the ability to differentiate different size lipoprotein particles seemed like it was clinically useful. But at the end of the day, as I've gone through, it turns out that it's really the particle number that matters. And if you can convince people to not pay so much attention to LDL cholesterol and pay more attention to APOB or LDLP, then you're better off. But that means that measuring the size of LDL doesn't matter. Oh, that's a bummer because we have a great efficient way of doing that. So then the question was, well, are the lipoprotein subclass distributions useful for something else besides cardiovascular risk assessment and management?

52:26And the answer is absolutely yes. There is a well-known association between higher triglycerides and lower HDL cholesterol and insulin resistance and diabetes and insulin resistance leading to diabetes risk or leading to diabetes. So it's already known that there's a lipid signature for insulin resistance. And the idea was if we could measure the different sizes of VLDL, LDL, and HDL, could that do a better job than just the triglyceride over HDL cholesterol ratio? So the poor man's insulin resistance measure from a lipid panel is triglyceride over HDL cholesterol ratio. And Jerry Reven, who was really discovered and promoted the idea of insulin resistance being extremely important, he advocated that it be used because people were getting lipid panels and this information was not being used for anything.

53:23So with this LPIR score, which brews together six VLDL, LDL, and HDL size and subclass concentration parameters, it brews them into a score from zero to 100, higher scores being more insulin resistant. Then the question was, does this LPIR score do a better job in assessing whether somebody is likely to become diabetic? So the pathophysiology has to be talked about a little bit here because there are some interesting parallels to where we are today with respect to primary prevention of cardiovascular disease versus primary prevention of diabetes. So in the cardiovascular situation, as everybody pretty well understands, the initiating causal factor there is elevated cholesterol or elevated LDL or elevated WB, but it's acting over time.

54:18So it requires an integration of exposure over a long period of time. And that gradually leads to cholesterol deposition in the artery wall. That's atherosclerosis. Atherosclerosis over time starts little, more, more. But that doesn't trigger any clinical concern. This is a subclinical manifestation of cholesterol doing its dirty deed over a long period of time. And then at some point you might have a myocardial infarction or a stroke. And that's when the atherosclerosis has transformed into the clinical event. The good news from a measurement biomarker standpoint is that the initiator, the causal factor is cholesterol, which is easily measured.

55:04So now what about diabetes? Diabetes is very similar. It's a time-integrated process where if you are insulin resistant, over time, your beta cells have to spit out more insulin to keep your glucose under control. So you're making the beta cells work harder, if you want to think about it that way, if you're insulin resistant versus insulin sensitive. And so insulin resistance times time leads gradually to hyperglycemia, elevated glucose. So if it's 90 or below, you're A-OK. But over time, if you're going to convert to diabetes, you go through a transition of the glucose going higher and higher until it crosses this magic 126 milligram per deciliter line that defines diabetes.

55:52Peter Attia:Just for folks to know, we're talking average. Absolutely. Yeah. So the causal factor is insulin resistance. The thing that's sort of equivalent to atherosclerosis on the CBD side is hyperglycemia, sub less than 126. So not diabetes, but pre-diabetes. So when glucose gets over 100 before it goes from 100 to 126, you're pre-diabetic. guess what's easy to measure? Glucose. So in that case, the effect of the cause is measurable. The cause itself is not. And so what that means is that from a prevention standpoint, what you really want to do is to keep insulin resistance from transforming over time into hyperglycemia and ultimately diabetes.

56:41If you don't know that you're insulin resistant, You just, you're waiting for the easily measured glucose to become elevated. And now once that happens, you've lost about 50 % of your beta cell function. So the opportunity for real effective prevention, primary prevention, primordial prevention is to act on people whose glucose is okay and hasn't gone to this transition yet because the beta cells have started dysfunctioning.

57:12Peter Attia:So Jim, I want to pause you there because the way you've laid that out is very elegant. And I like the, I've never thought of it the way you just explained it. So I'm repeating it just as much for me as for others. People who listen to this podcast know we constantly use the way you describe the CBD prevention thing, which is you have a causal marker. You don't need to wait until disease is measurable to treat it. And the example I always give is smoking and lung cancer. We have a causal marker, for lack of a better word, smoking. We always have to specify causality doesn't mean one-to-one mapping.

57:50Peter Attia:There are some smokers that never get lung cancer. There are some never smokers who still get lung cancer. None of those facts erase the causality of tobacco and lung cancer. Do we need to wait for a smoker to develop up a small cancer to tell them to stop smoking. Absolutely not. That would be malpractice. You always eradicate causal drivers of disease the moment they appear. And that's why when you have elevated LDL-C or ApoB or LDL-P, you treat it immediately, not once they have disease. Same with hypertension, same with smoking as it pertains to cardiovascular disease. Now let's pivot to what you said, which is, look, we know that insulin resistance is the cigarette to diabetes as cancer.

58:38Peter Attia:Do we want to wait until we actually see the glucose rise, which by the way is the biomarker that defines diabetes, when in reality by the time that's happening, there's potentially already cellular damage at the level of the beta cell in the pancreas and it's basically running out of steam. And what else can we measure? Now, I want to come back to the idea of there are things that we can do, but they're very laborious. So an oral glucose tolerance test is a fantastic way to find out that canary in the coal mine years before it shows up. But it's so fallen out of favor as a clinical test because it takes two hours.

59:16Peter Attia:I mean, it's just so cumbersome to do that outside of our practice and a few others, I just don't imagine many people want to do it. So Jim comes along and says, what if somewhere in this NMR spectrum is a whole series of things that turn out to be a fantastic marker for insulin resistance that we can use as causal proof that you're on the wrong path before your glucose goes up? Yes. And it seemed to us a very compelling case that you would want to act on the causal factor and not the effect or the downstream effect of the action of insulin resistance. But it's interesting. I mean, back to convincing people, clinical translation.

1:00:08I mean, I use that word. I don't think I was familiar with that word when I started this company called Liposcience to try to get NMR testing introduced into clinical laboratories. But I thought the argument of LDLP versus LDLC was quite compelling and that people really should be using it as the biomarker to determine management of LDL. Tremendous resistance by the establishment. I could never understand why they were so resistant. The messaging people learn about cholesterol in medical school, oh, we'd have to tell a different story and people wouldn't get it and all sorts of reasons that seem pretty weak to me.

1:00:48So on the diabetes side, it's the same thing. People imagine that hyperglycemia, prediabetes, is a risk factor for diabetes. It's not a risk factor. It is the disease. It's just in a less manifest form. So why not address the cause? A lot of resistance to that. It sort of blew my mind. And we did NMR analysis in a number of studies to show the efficacy of, or at least the relationship between insulin resistance score and likelihood of future diabetes. And by the way, transitioning to pre-diabetes by no means means that you're going to get diabetes. I mean, my wife has been pre-diabetic 105 or so milligram per deciliter for 25 years.

1:01:39It doesn't change. So the insulin resistance score can tell you whether you're more or less likely once you're pre-diabetic to transition. And then at that point, even though you'd like to have intervened earlier, it's not too late to still do something about it.

1:01:54Peter Attia:And so, Jim, was the LPIR score validated on longitudinal data to predict the development of type 2 diabetes? Yes. Okay. So in that sense, we'll talk about MVX and how similar that was in that regard. Has there ever been a comparison that says how well does it do versus an oral glucose tolerance test? yet. Yes. And so, well, not oral glucose tolerance, that's because in the real world, it's not being used. Too complicated. So you're really... Fasting insulin is sort of an easier way to assess insulin resistance. It has some analytic issues and practical issues. So nobody has really been interested in using insulin for that purpose generally in clinical practice.

1:02:37But the LPIR score has been compared to fasting insulin and is better. The study that shows it The best is one that unfortunately hasn't been published yet. It got very close to having the manuscript be written. It's in the diabetes prevention program, so you couldn't ask for a better study because this was a study that put intervention, lifestyle intervention, metformin, on the map in terms of being able to do something about transitioning to diabetes once you had hyperglycemia, once you were pre-diabetic. So this study was done about 20 years ago. We have baseline samples and then one-year samples post-treatment.

1:03:18Everybody in the study had an NMR analysis done. LPIR and other things that we can measure by NMR that make FPIR better if you want it to be better were shown to be independently predictive, better than insulin. But most importantly, because that was an intervention study, lifestyle change and weight loss was shown to significantly reduce the incidence of diabetes in these people. Metformin, less effective but significant compared to placebo. So what we were able to show really nicely is that the LPIR score was reduced significantly by lifestyle, less significantly by metformin. Branch-chain amino acids, which we haven't talked about yet, but higher branch-chain amino acids are also related to insulin resistance and can improve the LPIR score.

1:04:13And we actually have developed another score called the Diabetes Risk Index that integrates or adds branched-chain amino acids to LPIR.

1:04:22Peter Attia:Okay. I was going to ask you that. So just to confirm, the DRI, the Diabetes Risk Index, is the LPIR score inclusive of the three branched-chain amino acids. Right. And when do you recommend using one of those versus the other? Well, you know, it really never has gotten to be far enough along to have the amount of acceptance that that discussion has even occurred. Is DRI commercially available with LabCorp now? It is. It is at LabCorp. Okay. We should also point out for folks who are wondering, your company, Liposcience, that you founded in mid-90s was acquired by LabCorp 10 years ago, a little more than 10 years ago.

1:05:01Peter Attia:And so for those of us like me, the dinosaurs, is like we used to still order a liposcience test. Now it just all happens through LabCorp. So presumably that has increased, I assume, some uptake of the test. So I wanted at some point to get into the question of why hasn't broad clinical translation occurred? Because it hasn't. Right now you can only go to LabCorp for this information. So I'll go there now briefly, if you don't mind. So liposcience began, as I told you, as a spinoff of the university. I left the university. And we tried to convince people that size didn't matter and LDLP did matter.

1:05:44And we did that for quite some time. And we were a laboratory testing company. And so samples were sent to liposcience and you'd get the results back. But the ambition -

1:05:54Peter Attia:You had beautiful results. And that color report. I loved it so much. Why not use color printers? It was beautiful. But the business objective was never to be a lab that got bigger and bigger and did more testing. It was to make the ability to do NMR testing available to any laboratory in the world. So we started by what was available. I talked about NMR machines being in every chemistry department. These are research NMR machines. They're engineered for multifunctionality. You can do any weird NMR experiment. There's lots of variations on the theme. We wanted NMR to do one thing very efficiently and as rapidly as possible.

1:06:35And so ideally, 30 seconds or less, pop one sample in, automatically another sample comes in, boom, boom, boom. So we realized that we couldn't use a research NMR spectrometer for that purpose. We used them for the initial years at Liposcience because that's all there was. But we wanted to transition from a lab testing company into an IVD company, in vitro diagnostics company. All laboratories rely on IVD companies to supply them the machinery and the reagents to do all these assays. So LabCorp does 3 ,000 assays. They rely on other people, Roche, other people, to provide them with the wherewithal to do the testing.

1:07:18Those are IVD companies. We wanted to be an IVD company at Liposcience, make an NMR analyzer that looked just like a regular chemistry analyzer to a med tech that had no experience, no knowledge of NMR, walk up to it with 200 samples, present the tray, push the green go button, and walk away. So that's what we actually did. And a lot of investment and a lot of time and effort was put into that. And that is the Vantara NMR analyzer. It's the only existing NMR analyzer in the world. And we went to the FDA because these analyzers have to be FDA cleared in order to go into different laboratories. That was an adventure because what did FDA know about NMR spectroscopy?

1:08:03And it was a new platform, a new way of testing that had to be understood. So anyway - When did you get the CLIA approval? We got the, no, not CLIA. We got FDA clearance for LDLP and the Vantara analyzer in 2011.

1:08:18Peter Attia:Oh, wow. Okay. Yeah, 2011. And so these Vantara analyzers, a number of them were manufactured. And then about the time that just before, a year or so before Liposcience was sold to LabCorp, these analyzers were started to be distributed to major laboratories. LabCorp was one of the recipients of these analyzers so they could do the testing in-house rather than having to send the samples to liposcience. Clinical laboratories hate sending send-out samples. And when that's for a rare cancer or something, when it's a fairly rare event, no big deal. If you're doing hundreds of these a day, you don't want that hassle.

1:09:02So they were very happy to receive the Vantara analyzer so they could do the testing in-house. And every time they did, they would pay liposcience for each analysis. That was the business model. We also made Vantara analyzers available to the Mayo Clinic, Cleveland Clinic, Scripps Clinic, Arup, a big reference laboratory in Utah. So we were on the way to making it available broadly because we didn't want to be the only people that could do NMR testing. And also we wanted to convince people that this wasn't magic. It was real. And it was analytically, in many respects, much better than reagent-based chemistry testing.

1:09:43Unfortunately, by that time, liposcience had gone public. I was no longer on the board. I was basically the investment that we needed to start liposcience. We basically took too long to get to the payout of the initial investors. So the investors were not patient. They were very patient up until then, but you almost couldn't blame them because they wanted to get their money back, venture capital in particular. So the company went public and then LabCorp came along and said, we'd like to buy you because we can make more money if we don't have to pay whatever, number of dollars to liposcience every time we do this test.

1:10:21So it was a purely financial decision. LabCorp really didn't care about measuring things other than the NMR lipoprofile, the LDLP, et cetera. It was financially based. That was too bad for the vision of having NMR analyzers in every laboratory because LabCorp is a lab testing company. It's not an IVD company. So too bad the IVD business model, the IVD vision was ended in 2014. So yes - Did you join? Did you go and become a scientist? The research group largely was retained by LabCorp. So we had a pipeline of things like LPIR and DRI, things that were coming down the pike that we'll talk about later.

1:11:08So really, the best was yet to come in terms of the clinical value and the things that could convince people that having Vantara analyzers in their lab was a commercially useful thing, but also a clinically useful thing. So the bad news is that LabCorp, understandably not sharing the IVD vision and being a laboratory that wanted to have NMR be proprietary to themselves, took back these analyzers that had been placed in these other reference laboratories. And then, unlike liposcience that knew that when it developed a new test, it needed to go to the trouble of creating awareness and interest and doing the studies to prove the clinical efficacy of these tests.

1:11:54That's a very important activity. LabCorp doesn't do that because they're not an IVD company. They are reactive. They're not proactive. They're very good at being reactive. And in COVID, they ramped up the COVID testing. So I'm not saying that LabCorp doesn't do a really good job at what they do. But they really didn't know what to do with new knowledge that they generated in-house by acquisition of liposcience. And so there was no marketing, no awareness creation. And basically, liposcience went invisible and is still largely invisible. Most of the testing is still done by the people that were interested in the testing, thanks to liposciences efforts.

1:12:41So these X number of analyzers that were produced 15 years ago are what LabCorp is using to produce this information and this more exciting new information.

1:12:53Peter Attia:And what's the life of these analyzers? Good question, because nobody's ever... So the magnets themselves, these superconducting magnets, last a long time and don't degrade. But they're using PCs, 10-year-old PCs. And anyway, all the moving parts. So they're not going to last much longer. And this is what maybe I'm most concerned about and most interested in people hearing about because what needs to happen for this to continue, this clinical translation to continue at LabCorp, but also ideally broaden to the rest of the world, is for an IVD company to come in and basically acquire the liposcience technology.

1:13:43And there's a lot of patents, intellectual property associated with this. A lot of expertise that comes from, in terms of service and keeping these machines functioning well. And we've done many millions of tests and you learn by doing. So what people don't know is that at some point in the future, maybe sooner rather than later, these Vantara analyzers, first generation, only generation, are going to cease to function. And it would be a real shame, especially with the things that we're going to talk about that are more exciting than LPIR and in my mind, LDLP, et cetera. So there's an issue here.

1:14:24There's a problem, and hopefully it'll be addressed by an IVD company taking over. I've tried to make the case to big multinational IVD companies, but NMR is too exotic. I mean, I basically show them that this is a completely de-risked proposition because we've already gone through the regulatory hurdles. We've already made the analyzers. We already have the experience. So it's not like starting from scratch with something that you're unfamiliar with, technology you're unfamiliar with. But these big companies have a lot of inertia and talking to the right people or having these big companies be entrepreneurial.

1:15:00So I think this is maybe, if it happens, it'll be possibly a smaller IVD company or a new startup IVD company basically taking the torch that Liposcience -

1:15:12Peter Attia:And has Liposcience said that they would be willing to sell those assets, the IP? It's LabCorp that owns all those assets. Sorry, that's what I mean. Has LabCorp said that they're willing to sell that IP? And I think they realize, well, yes, they're interested in licensing the patents. Most of the patents have to do with the assays, so LPIR and MBX that we'll talk about. So absolutely. But who's going to license them if there's no machinery to produce the information? So what is your estimate of the cost per machine now if you were going to make a Gen 2? Well, so these machines cost on the order of$400 ,000 or$500 ,000.

1:15:53But the beauty of it is, of course, that they last a long time, which we've shown, but they have no consumables associated with the assay. So it costs just as much to do 10 assays a day as 1 ,000 assays a day. So in a high volume setting, these tests are very cheap. And what we've talked about, we talked about LDLP and the NMR lipoprofile was the report that gave people the LDLP information. But that comes from this simple NMR spectrum that can then use to extract much more information than LDLP, LPIR, DRI. It's just part of the story. There's all these other things that we'll talk about later.

1:16:34So in terms of efficiency, analytic efficiency, you couldn't ask for anything better because if you want to use NMR to produce a lipid panel and actually after telling you that NMR only measured particles and not cholesterol, the basic information is encapsulated in all these NMR signals. So you can actually feed NMR data to a machine learning algorithm, AI, and train it to produce accurate lipid panel and ApoB information. So we published this four or five years ago that you can actually use the NMR spectrum to produce an extended lipid panel, ApoB plus lipids, no incremental cost to a lipid panel.

1:17:21The ApoB doesn't add cost. It more than doubles the cost of a lipid panel if you want to do it by chemistry and use regular reimbursement in the U.S. So it's extremely analytically efficient and cost effective. The thing that people probably don't appreciate and I didn't appreciate as a naive professor, I thought, well, surely this will be commercially attractive if it can cut the cost of doing these tests. The cost of these tests is so much smaller than the price that is charged for these and the insurance pays for these tests that cutting the price, the cost in half of doing the analysis makes no difference whatsoever.

1:18:04So this is, you know, we'll maybe get into this later because what is true is that NMR, a single scan can tell you a heck of a lot more than just your cardiovascular risk, your inflammation level, your diabetes risk, your mortality risk. This all comes in the same assay, essentially.

1:18:28Peter Attia:Well, I can think of no better way to introduce the MVX assay. So I'll tee it up for you and then take away the story. So by the way, I went to have my first MVX drawn. We've been doing it on our patients for about four months now, and I just haven't got around to doing a blood test on myself. So I went out to LabCorp two and a half weeks ago because I wanted to have the results back when we were sitting here. And wouldn't you know it, Jim, they screwed up the assay. So I don't have it. They got everything else. Every other test we ordered, they got, but they butchered this one. So I don't have my own to talk about.

1:19:04Peter Attia:But the MVX is a composite score that measures, if I recall, six things. So small HDLP, glycate, which we haven't introduced yet and we'll talk about, citrate, and the three branch chain amino acids. So leucine, isoleucine, and valine. So why don't you tell us the story of how you developed this score? And I'll just give the punchline so the listener knows why they should be paying attention. This score seems to have remarkable, when it's normalized to a zero to a hundred number, the higher the score, the worse it is. this score seems to have remarkable predictive value of all sorts of things we want to avoid, starting with death, cardiovascular death, liver disease.

1:19:54Peter Attia:While the first study that I saw was done in a very, very high-risk group of catheterized patients, and it would be easy to dismiss that it was only valuable in that population, it's been demonstrated in healthy populations as well. So tell us about the score. Yeah. So it's a really interesting story. And one question that gets asked is, well, why did you think of measuring those things? Or did you have some mechanistic reason for focusing on these things that ended up contributing to this MVX Metabolic Vulnerability Index score? And the answer is no, that's not how it happened. And I do want to make this point because MBX, and especially MBX, raises more questions than answers right now.

1:20:45So you really would like to understand if it predicts mortality so well, why does it do that? Is it causal? Can we intervene? These are the things that really matter clinically. So it wasn't starting out with some idea that branched gene amino acids are really important mechanistically, even though they are. This was because NMR analysis has given us a very efficient tool to measure baseline samples from studies that follow people over time, 10 years, 20 years, 30 years longer, to see what develops. And so you really would like the ultimate idea on a biomarker is that it predicts the future. You want to know not if it's related to cardiovascular disease, but is it related to getting cardiovascular disease in the future?

1:21:40And so we have availed ourselves over the years of baseline samples for many very large clinical studies. You mentioned MESA, Multi-Ethnic Study of Atherosclerosis. That's a NIH-funded study that started in about 2000. It's had more than 20 years of follow-up now. We were asked to measure the baseline samples 15, 20 years ago for free. Now I work for a company that has to make some money, but I had enough freedom to be able to offer that test. Initially, I asked for money, but then they finally said, well, we just can't find the money for that, but we'd really like to have the information. So it was measured for free.

1:22:20Same thing from other very large studies. Women's health study, 26 ,000 women at baseline in a study that's been followed up for many, many years. Framingham Offspring. Many clinical trials, Framingham Offspring study, all sorts of intervention trials, Jupiter, et cetera, the Diabetes Prevention Program. I would like to quickly go back to that just for two seconds because we didn't finish the thought of what the Diabetes Prevention program, even though it's not published, tell us. And the important thing that it told us was not just that LPIR score goes down with lifestyle intervention and branched-team amino acids go down, which is all good thing, but that those things going down, the delta between where it started and where you got after the intervention, very powerfully predicts incident diabetes or or the lessened diabetes risk.

1:23:15So that was sort of the missing link. If you really want to argue causality, you really want to show that lowering LPIR translates to lower diabetes risk. And it does. It's quite powerful. It hasn't been published yet in part because the primary author died recently, unfortunately. And so it will be eventually. All right. But that's one study. So we've gone out of our way over time to make it possible for people that didn't have funding to get NMR data on baseline samples for observational studies and intervention studies going forward. MESA has been particularly useful to us, 7 ,000 people, baseline NMR.

1:23:58And because we supplied the assay for free, the NIH has given us access to the information about who developed cancer, who developed cardiovascular disease. et cetera, dementia, et cetera, all sorts of outcomes. And so the beauty is that once we have taken the NMR spectrum and gotten from it, what we initially wanted to get from it, let's say LDLP, these spectra are sitting there in a computer stored on a disk. And when we develop the wherewithal to extract new information from the NMR spectrum, we can go back literally in a couple hours to interrogate a very large clinical trial and get prospective information as to whether it's predicting a particular outcome.

1:24:49So back to MVX, that's exactly what we did in this cardiac catheterization study at Duke University. 7 ,000 people over a period of years recruited into this biorepository. When they came to the cath lab, when they came to the cardiologist with chest pain or some issue that qualified them to have coronary angiography done. And their blood was taken at baseline and it's stored at minus 80, where it's perfectly stable for NMR analysis. So we got a relationship with Duke to obtain those samples, do NMR analysis. And we found in published papers that small HDL particles were particularly powerful in relating to the likelihood somebody would die during the roughly five or 10-year follow-up of this study.

1:25:40And then there was another thing that we could measure by that time called glycate, and we might as well talk about glycate now. It's another signal in the NMR spectrum that's not where the signal is that we interrogate for LDLP. And so for 10 or 15 years, we didn't care a whit about any of those other signals. But a funny thing happened with where the glyc-A signal is. It was basically superimposed or in the way of another broad signal that we were trying to interrogate to learn about the fatty acid composition of somebody's plasma, how much monopoly and saturated fats were in the blood. And to interrogate that, the signal, the sharp signal was on top of that and was getting in the way.

1:26:27So we basically worked out a way of quantifying how big that signal was so we could subtract it from the other signal. But that was perfectly good quantification information. So that signal, which I wouldn't have thought to do anything with, the person who does all my epidemiologic analyses, Irina Shalarova is her name. We had the MESA data sitting there. We could go and very quickly use the software we had developed to measure this sharp signal. And she just said, well, does it relate to anything that happens in the future in MESA? Oh my gosh, it relates to all sorts of things, including mortality very, very strongly.

1:27:12So what's up with that signal? Well, it turns out, going to the literature, this often happens. 20 years ago, somebody published a paper saying that the signal was an inflammatory marker. It actually comes from the carbohydrate, the glycan decoration that's on a lot of proteins in the blood. And most of these proteins are so-called acute phase proteins that increase in concentration in inflammatory conditions. And so even though this signal was not telling us which acute phase proteins were contributing to it, it was a composite. And not only did it essentially quantify the most abundant four or five acute phase proteins that contributed to this signal, But this carbohydrate decoration, this glycan decoration, is used for all sorts of purposes, signaling of different types, et cetera.

1:28:11So there's very complex. People worry about the glycome. It's like the proteome in the genome. There's a glycome. I know nothing about any of this. But one thing that we know where the signal comes from, and it comes from particular sugars on this carbohydrate. And inflammatory conditions, more of this decoration is put on some of these proteins. So actually, this glycase signal is reflecting not only the levels of these acute phase proteins, but how much glycan is on them, which is also connected to inflammation. So it turns out, rather miraculously, that how big this signal is, is a very useful measure of your steady state, of your systemic inflammation level.

1:28:57It's a very stable parameter because it's the integration of lots of different things. So rather than CRP that you typically measure clinically to assess inflammation, it's very volatile. It goes up and down day to day. So all clinical recommendations for the use of CRP information say that you should take the average of two or three different measurements. Nobody does that. That's the recommendation to get around some of this biological variability. GlycA doesn't suffer from that problem. That's probably in large part the reason that when added to CRP in a prediction model, it typically assesses the outcome more strongly than CRP does.

1:29:43But CRP tends to independently add. So inflammation is a very complex thing. this is a very unspecific marker, but it's a very stable and useful clinically marker of systemic inflammation.

1:29:58Peter Attia:Does it include, do you think, or capture what we see in the various interleukins? Yes. So it's correlated strongly with IL-6 and other interleukins and these correlations. But that's all we know. I mean, so yes. And you'd really like to be more specific. And if there's local inflammation as opposed to systemic inflammation, this is not going to tell you anything. But what it does do is offer you a simple and very cheap, because it comes along for the ride with the other NMR information. If you quantify this glycase signal, you have a very powerful marker of the things that systemic inflammation contributes to.

1:30:39Peter Attia:Now, using HSCRP as an example, Jim, which, as you pointed out, is going to rise with inflammation. One of the things that clinically we pay attention to is how high is it? So if I see a brand new patient and their HSCRP is two or two and a half, in many ways, that's more disconcerting to me than if it's 40. because the 40 is so high that I know it's really in response to something acute. They're probably getting over a cold. Maybe they got a vaccine two, you know, four days ago or something like that. Now, of course, that doesn't obviate the point you made, which is I still want to see longitudinal data.

1:31:24Peter Attia:Like I can't make it, I can't assume the two is bad because I could also be catching something on the way down or on the way up. It could be three or one depending on the next day. but it's usually the case that when something is very, very high, it really speaks to acute inflammation, which is less pathologically concerning. These low simmering ones that I see, those are the ones that give me pause. Is the glycate the same as that? No, it's very different than that. So CRP levels could go up a thousand fold on an infection. Glycate levels, the response is much more muted. Because it contains so much information in it, you think?

1:32:01I can't really answer the because as well. It's just the observation. Just the observation. And it's true that if somebody has an active infection and you're trying to relate the glycate level to mortality in people that didn't have an infection, you're going to be misled by that. It'll be higher by twofold, not a thousandfold. So it's not immune to those changes. But you wouldn't want it to be because it's an inflammation marker. Yep, yep. So in people with inflammatory diseases, rheumatoid arthritis, psoriasis, et cetera, glycogen levels are significantly elevated. And they are responsive to anti-inflammatory treatment.

1:32:39And so it has all the characteristics of a useful biomarker to assess not only things that you would like to have some visibility to, like systemic inflammation. I think we're coming to understand that that's an awfully important contributor, even though we don't understand the mechanistic fine points to so many things, including mortality risk. So anyway, I just wanted to tell you that the way that we discover these biomarkers is different than the way other people discover a lot of biomarkers. So it's more the top down. We have a very efficient way of doing the epidemiology. So we know already that these markers have a strong relationship to human health conditions.

1:33:28A lot of the work that's done starting from bottom up with a mechanistic idea and a particular enzyme that you might maybe want to target as a therapy, you have to then do animal models and then you go up to humans. And then you have to do expensive trials. And then you typically measuring these things is not as easy as it is to measure these things by NMR. So it's a completely different approach to discovery. It's sort of irrational discovery because you're basically using these large databases of population studies and then discovering things that you really didn't go after discovering in the first place.

1:34:07Peter Attia:So there are three components to the MVX. You've already talked about the lipoprotein one, which is the small HDLP. You've just explained the inflammatory one, which is glycate. The third one is sort of the metabolic one, which has the citrate and the three BCAAs. So how did you come to figure those out? So you're right. So actually with the Duke collaborators, there had been a paper published showing that there was a glycate paper predicting mortality. The interesting thing about this cath gen population is they came to the cath lab because they had some presumed cardiac issue. In the study that we did, for five-year mortality, 17 % of the people died in five years, and they were about mean age of 60 coming in.

1:34:56So that's a pretty high -

1:34:57Peter Attia:How many? 15 %? 17%. 17 % mortality, five years in people that were not elderly. three or four times higher for sure than a regular population. So the presumption was that these are people that died of cardiac causes, but less than half of them died from cardiac issues. 60 % died of non-cardiovascular causes. So the major thing that happened - So 60 % of the 17 people, 17 % of people who were dead by 65 were non-cardiac causes. Yeah. So anyway, mortality was the most prevalent outcome. It wasn't a new myocardial infarction or a recurrent myocardial infarction. These people died. And so glycate predicted it, small HDL particles predicted it.

1:35:52We might talk more about that. And then we simply looked at all the other things that we had learned to measure. And we had just kind of started this activity. So branched chain amino acids, isoleucine, leucine, and valine, ketone bodies, plasma protein. I mention those two things because when you look individually at those things, ketone bodies and plasma protein, they have significant associations with mortality. So why didn't we use those as part of the MVX composite biomarker? Because we were looking for things that contributed independently and additively to the other things that we've already talked about.

1:36:32So the inflammatory part, glycate and small HDLP, we created a subscore called the inflammatory vulnerability index, IVX. The other four seem to relate. So we discovered that these branched-chain amino acids and citrate, independent of glycate and small HDLP, added to the prediction of mortality. Then it was, okay, why? And so then we went to the literature. So we really approached...

1:37:03Peter Attia:And you're doing all of this inside of LabCorp. So LabCorp, at least still at this point in time, had the appetite for the R &D. Yes, although really, yes, they paid our salary, but that was... We were sort of left alone in this little building to continue what we were doing at at Liposcience. So thanks to LabCorp for not getting rid of everybody. So then it was going to literature, trying to figure out, does this make sense biologically that these things might be related to mortality? And that's where you come to the literature that very powerfully speaks to the great mortality risk that people with several acute diseases, is especially kidney disease and dialysis patients, heart failure patients.

1:37:51Peter Attia:Anything with cachexia, I could see increasing. Anything with cachexia, so does sarcopenia, old people. So there's a lot of literature, especially in the kidney disease literature, that describe this vulnerability as coming from a so-called malnutrition inflammation syndrome. And it's called many other things, protein energy wasting syndrome. So the cachexia, the wasting syndrome is part of this, but inflammation is part of this. Inflammation is probably the context that allows these dysregulated metabolism situations to exist. So the fact that malnutrition inflammation syndrome, we had the inflammatory parts, we speculated, but the branched gene amino acids were related.

1:38:46And they were related in the opposite direction that branched gene amino acids are related to diabetes risk. So high branched gene amino acids speak to insulin resistance, obesity, diabetes risk. Low branched gene amino acid levels speak to mortality risk. And we can talk more about, You know probably much more about why this makes sense in terms of mechanism, because it's partly related to mTOR signaling and the whole skeletal muscle.

1:39:21Peter Attia:Yeah, turnover of amino acids and muscle protein synthesis. et cetera. But it was satisfying to find this literature and to say, oh, maybe these are just better biomarkers of something that's already understood in the acute clinical context. But in the Ketchum population, nobody had described this in a cardiovascular cohort. And then, so we said, well, this exists in spades, apparently, in this presumed cardiovascular cohort. But then we did subgroup analysis within this 7 ,000 people in the cath joint study. And we asked, does this MVX association with mortality exist equally strongly in men and women, in people with and without obesity, in people with and without diabetes, with and without heart failure, with and without a previous myocardial infarction, with or without coronary catheterization, occlusion of the coronary arteries, with and without kidney disease.

1:40:30And it's not affected by any of those things. It's equally strong, if not stronger, in people without the chronic disease versus those that are disease-free. So in fact, the strongest relationship of MVX to mortality, you mentioned how exquisitely strong it is. This was looked at by, well, in different ways.

1:40:51Peter Attia:I thought that the hazard ratios were bigger in the cath study than in the MESA study of healthy people. That's true. That's true. And the reason is that for whatever reason, if these people came to the cath lab and they were enriched in people who ultimately suffered from these wasting syndromes. Okay. And the fact that you see the same prediction, if not as strong, for sure, in people with absolutely no evidence of any chronic disease. In fact, the studies that were most recently published and the most interesting one that I'll mention is one that's not yet published but is about to be submitted for publication, MVX in young people, 30-year-olds.

1:41:39Would we expect to see the relationship? And you do see the relationship, but it's weaker. And I didn't sort of finish that. So the two subparts of MVX are the IVX inflammation part and the other four parameters brewed together to form MMX, metabolic malnutrition index. It's kind of an arbitrary thing to talk about these two parts of MVX because it's known that there's synergy between these. It's a syndrome. It's interrelated, intertwined. But on the surface, at least, it looks like it might be useful to take a high MVX score and it might be due more to inflammatory reasons than the metabolic malnutrition wasting reasons, in which case different therapies might be better suited for that person rather than somebody with the same MVX score with a more malnutrition issue.

1:42:37So we thought it might be clinically useful. That's why we did that.

1:42:41Peter Attia:Are they both, I know the aggregate score is reported zero to a hundred. Do the two subscores get reported that way as well? Yes, same way. Okay. So you could say Mr. Smith is a 75, which is very high risk. But when I look at his aggregate score, his IVX is only 25, his MMX is 80, this is the issue. It's the sarcopenia and the wasting that is really driving his risk. It's not so much inflammation in this case. That's a possibility. I don't think that that possibility will ever be found to be that different. So usually these are more, because it's a syndrome. Got it. Yeah, the numbers I used are wrong, but that's the spirit of what you mean.

1:43:25Peter Attia:Absolutely. That's the idea. We've learned a lot since this 2023 first publication about MVX in this cardiac catheterization cohort. The important thing that we did there, I was advised that I really shouldn't try to publish this until we had replication because these hazard ratios were so dramatically different from low and high MPX scores. So in that paper, we reported a completely independent cardiac cath population in Utah, Salt Lake City, and it replicated very well. And since then, we have been interested in seeing if it replicates in other disease populations. And as we referred to, what about people with no overt disease whatsoever, younger, older?

1:44:17Peter Attia:Well, let's talk a little bit about that. The one that just came out, of course, was the Masl-D paper. Is that the one you wanted to chat about? No, actually we could because these papers are coming out now. They're just coming out at a geometric rate, which is great. The reason is that we just have to go back to existing NMR data. We're mining other people's costly studies who were piggybacking on their work, their funding, and getting very quick gratification about whether MVX is good for this or that or the other thing. Is there one in an intervention study? Because that would be the next step.

1:44:56Peter Attia:Maybe you've already done it where you say, look, we've got an MVX at baseline. We're going to treat you guys placebo, non-placebo. You know where I'm going. Absolutely. That has to happen. It isn't going to happen if nobody knows about MVX. And the thing that has been a little disappointing to me is how little, I mean, nobody read that 2023 paper. I was very proud of that paper. I thought there was a lot of meat in that paper and a lot of implications clinically and otherwise. But people read papers that are called to their attention. And this is part of the problem. If this was a liposcience, if we were at liposcience, we would be promoting awareness of this paper in meetings, et cetera.

1:45:43And this isn't happening at LabCorp. Okay. So, but now this will help. Yeah. I mean, there's going to be a lot of people that are listening to us. Right. And so, yes, we would, that's the next step. What will be published this year will be, I think, sufficient for anybody to see the replication of the phenomenology fairly quickly after that first paper in heart failure patients, we could show this. The other thing that was kind of nice about that heart failure population is they also had a lot of frailty information. A lot of these were older people with heart failure in Minnesota. and so they were able to calculate frailty scores and also biological age by, well, not in that study, but another study of older people.

1:46:38So some of the other things that are talked about a lot about relating to longevity and so on were measured in these studies that we've been able to look at MVX in. And in terms of frailty, physical frailty, correlation, but fairly weak with MVX at a 0.2 correlation coefficient. So it isn't like you need to have physical frailty to see the MVX be high or vice versa. And in terms of mortality risk, frailty, physical frailty on top of MVX score adds considerably, but MVX very powerfully still in the presence of frailty score predicts mortality.

1:47:19Peter Attia:And is it always a five-year look forward? No. And in many studies, it's longer than that in part because you want the statistical power from more people having the outcome. But there's something powerful about the short window. I mean, in many ways, that's actually a feature, not a bug if you can offer that insight because we don't have many short-term predictive biomarkers. Absolutely. And actually, the one study that I was sort of confusing with the heart failure study is a study called EPIs. It's a study of older people with lots of things being measured, including these biological age measures by chemistry assay, not the epigenetic flavors.

1:48:03And a couple of years ago, I think it was 2022, they had NMR information and then they had all this frailty information and 186 different variables. And it was done by some epidemiologists in Minnesota using the most high-tech ways of trying to deduce whether the associations were causal or not. I'm not really sure that that really, to me, demonstrated that. But they use methodology that purports to assess causal relations. And out of those 186 things that were looked at, small HDL particles were the most powerful at predicting two-year mortality in these people.

1:48:48Peter Attia:What would be a blind spot? Where does it get fooled? We already gave one example, which is if you're in the throes of a brutal infection, you're getting over a cold, that could artificially elevate, although not to the same extent as CRP, the glycate, that could offset it. Have you seen other false positives, so to speak? No, I mean, and we haven't really looked in ways to possibly see those because we've looked overall at the prediction in a population and these people at baseline either have this, that, or the other disease. Yes, it gets kind of canceled out in the wash at the population level.

1:49:22Peter Attia:Right, exactly. The question of whether it's causal or whether, so there's two questions. One, is it modifiable? So let's just, I've been speaking to the intervening three years since the paper was published. We now have really good data, some coming very soon in different disease populations that this replicates and is seen. It doesn't matter who you are, this relates to mortality risk. I want to come back to this later because you said something earlier about how MVX remarkably relates to not just mortality, but diseases that reduce mortality. So I want to quibble with that idea a bit later.

1:50:14But just to get back to the question of, are there interventions which lower MVX? And then could we do the study of that intervention to show that that's connected to a reduction in mortality risk?

1:50:29Peter Attia:Before you do that, Jim, I want to go back to the 30-year-olds because we didn't really finish the swing on that. No, we didn't. Are you able to talk about that or is that not published? I'm going to talk about it and I'm not going to say the name of the study. The draft of the paper is about to be submitted. It's just hard for me to wrap my head around the fact that any biomarker in 30-year-olds could predict anything. This is very true and that's why this is so interesting and novel. So the paper that appeared a couple months ago was from MESA. So we've been talking about MESA, 60-year-old people at entry.

1:51:08They weeded out all the people in MESA that had any self-reported or otherwise diseases, so restricted to healthy. And average age 60 or so. And MVX bi-quartile had this stepping stone relationship. Not as strong, the hazard ratios weren't as different as in cath gen, but very significant.

1:51:32Peter Attia:So that was the first - And do you recall in that study, Jim, what the difference was between the first and the fourth quartile in hazard ratio? Were you talking about like a 1.6? Unadjusted, it was about maybe, I might be confusing other studies, but adjusted, it was like 1.5 to 2. Okay. And we'll link to every one of these studies in the show notes. Yeah. But basically in otherwise healthy 60 year olds, the difference between being in the worst quartile, 75 to 100 score versus the bottom quartile, 0 to 25 would be about a 50 to - No, no, that's per standard of view. So it's actually greater than that.

1:52:14It's maybe two to threefold greater. By quartile, by top to bottom quartile. Got it. Wow. Okay. So big, big difference. Yeah. So, okay. So those are 60 year olds. So these are people. So my idea about MVX, people die when they're older. And so this thing, MVX comes into play when you're older and maybe MVX scores go up with age. MVX scores are virtually unassociated with age. What? Yes, unassociated with age. And the major proof of that is this 30-year-old study. So here we've got 3 ,000 plus people who were entered into the study between the ages of 25 and 30. So we have an NMR analysis that was done when the average age of these people was about 30.

1:53:05And this has got to be 30, 40 years ago because otherwise you wouldn't be able to do anything with this. This was 35 years ago. And there were blood samples taken at time intervals, more frequent than five years for the first few years and then five years after. So we have NMR data at year 10, 15, 20, 25, 30. So we know how stable the MVX score is over time. That isn't actually reported in this particular paper, but it's very stable. But the really interesting thing is that the distribution of MVX scores when these people were 30 years old is just as wide, almost identical to the 60-year-old people.

1:53:49There are people with low scores and high scores. And as you said, this is 30 plus years follow-up. So this is definitely premature mortality we're talking about. And these are all people that at baseline also were excluded from having any pre-existing comorbidities. So there's not only

1:54:10Peter Attia:young, but they're healthy young. We need to just stay on this for a moment, Jim. This is so counterintuitive. I just want to make sure not a single listener is failing to appreciate what you are saying. So I'm going to repeat it back and I want you to correct me because there might be errors where I'm oversimplifying. 35 years ago, we had a whole bunch of people that were aged 25 to 30 and we excluded all the people that had known issues. So if you had type 1 diabetes or you had some childhood cancer or whatever else, we didn't include you. We really looked at boilerplate, healthy 25 to 30 year olds.

1:54:42Peter Attia:We draw their blood. Every five to 10 years, we draw their blood again and we run the MVX assay on them. The first and most surprising potentially feature, certainly the first surprising feature when you're doing a bunch of MVX scores on healthy 30-year-olds is that any of them had elevated levels. Because the most obvious thing is MVX must at some level be a correlate with age, which is the single greatest predictor we have of mortality. And so big surprise number one is you could be 25 or 30 years old and have an MVX score of 75, which is very high. Okay. The upper quartile, the average was about 50-51 score.

1:55:25The bottom quartile was about 27. So that's the range.

1:55:30Peter Attia:So the distribution looks a little different than it looks in a 65-year-old population? No, but that distribution is identical to Mesa. I mean, it's identical. And Mesa was in 60-65-year-olds. Okay. And then you're saying not only do we have this distribution that mirrors that of people 30 years older, as we followed these people for 35 years, it predicted mortality. I am not aware. I'd have to think, Jim, but I don't think I can imagine a biomarker outside of a very extreme state. So you mentioned FH. Okay. If I know that I have two 30-year-olds and one has FH and one doesn't, and the FH1 is not treated, I can tell you with a very high certainty that person's going to be dead in 30 years.

1:56:16Peter Attia:This one will not, or very unlikely to. But outside of edge cases like that - Well, so that introduces the question, how did you get a higher or low MVX score when you're 30 years old? You didn't acquire it because of some vulnerability, disease vulnerability. You acquired it at birth. We don't know. We have to look now at younger people. We look at Framingham and Framingham offspring and try to tie that together? We have plenty of MVX data from studies where genomic information is available, epigenomic information is available. I mean, it's a great question. I mean, again, like I said, I've got questions more than answers, but it's really fascinating and novel and important, I think, for sure.

1:57:06And because of what we've just talked about, I want to go back to this issue of whether MVX has anything to do with whether you're likely to develop cardiovascular disease or diabetes or dementia or whatever. And what you will find already in the literature are papers that indicate or suggest that MVX does have those associations with the diseases, many diseases that cause mortality. But I think all of these are artifacts of the way the analysis was done. Because as you appreciate, almost all cardiovascular endpoint trials, as well as many other types of disease endpoint trials, cancer, whatever, kidney disease, they typically combine fatal and non-fatal events.

1:58:04So if you die of a heart attack as the first consequence of having cardiovascular disease, or if you have a myocardial infarction and survive it, these are grouped together in a composite endpoint called CBD. And when you look at cholesterol, it makes perfect sense because of the etiology, because of how the cholesterol is connected to cardiovascular disease and events mechanistically, that there's nothing wrong with using a composite endpoint. The etiology is the same. You get cardiovascular disease, you die from it. You get cancer, you die from it. So you want to have a biomarker that predicts whether you're going to get the disease, and then that automatically tells you what your risk is for dying of that.

1:58:53What this says is that maybe there is a separate influence on whether you're going to die from the cardiovascular disease or the cancer or whatever. And that's your metabolic vulnerability, your metabolic frailty, if you will. I like the idea of metabolic frailty because is frailty connotes susceptibility to dying. And even though people with high MVX score, like these 30-year-olds who have high MVX score, you look at them, they don't look any different than the people with low MVX score. So you don't see the frailty. But metabolically, it's there. It's basically setting you up to be more susceptible to dying from whatever disease or event old age that is going to contribute to your death.

1:59:43So dying sooner versus later is what MVX seems to influence, as opposed to getting the diseases that, quote, cause this. I tried to say this in the paper, but it's becoming much more clear now, especially with these 30-year-olds, that this is something that has to do with dying, not getting the diseases that cause the death.

2:00:09Peter Attia:So listening to you say this gives me an idea for a study that I'd love to see you do, Jim. So you're, I don't know how much time you spend in the oncology world, but I'm sure you're familiar with Keytruda. To my knowledge, Keytruda is the single best-selling drug of all time. And in many ways, it's been a miracle drug in oncology, the single most exciting development in cancer in the last 25 years. For folks unfamiliar with it, this is a checkpoint inhibitor. So people that have a PD-1 mutation that take this drug, regardless of what kind of cancer they have, this could be pancreatic adenocarcinoma, lethal cancer.

2:00:47Peter Attia:If you have this mutation, this drug basically takes the brakes off the immune system and your immune system eradicates the cancer. But here's the question. Why could you take two people that have the exact same PD-1 mutation, the exact same cancer by all intents and purposes, and you give them both Keytruda and one of them responds and one of them doesn't? We don't know. We do not understand what's happening at the immune level, to understand why that's happening. It would be very interesting for me to understand, and using Keytruda as an example, but you could do this with any therapeutic intervention where mortality is very quick, right?

2:01:31Peter Attia:And you could ask the question, does this become a prognostic indicator of not just mortality, but probability of success of an intervention. Yes. That's precisely what possibilities exist. When I first talked about this to people at Duke, the collaborators of the CathGen study, the people around the table, the first thing they said was, wow, this would be a great test for surgeons who are asked to operate on people who are frail or are less likely to survive the surgery or to benefit from the surgery. You'd like to be able to screen them for resilience somehow, but there are no biomarkers, good objective biomarkers to do that.

2:02:24Malnutrition, metabolic malnutrition, there are people I've read papers where people or surgeons are suggesting that people really should avail themselves of these interrogating whether somebody is metabolically or physically frail and has evidence of wasting. But if there's a metabolic component to that that is accessible via MVX, it could be very useful. And this is one of probably all sorts of possible applications. You mentioned this paper that was just published two days ago on MASLD. MASLD, yeah.

2:03:03Peter Attia:The artist formerly known as NAFLD. Formerly known as NAFLD. So liver disease. And the paper speaks very forthrightly about the possibility of using MVX for entry into clinical trials. Am I correct in remembering this paper, which I skimmed, so I'm ashamed to admit I didn't read the paper, But I could have sworn it said that MVX added predictive value to FibroScan in predicting subsequent fibrosis in the Masl-D patients. Does that ring a bell? I think that's true. I only skipped it as well. I was not a co-author on that paper. Yeah, it just came out. But, you know, as you well understand, so many clinical trials are expensive and are not done.

2:03:50Then many are not done because the events are too rare. Or too far away. Or too far away. And so being able to juice up your likelihood of people dying, for example. So having, but mortality is probably the endpoint that people care most about, right? And so it has sort of been a hierarchy of events. People care more about dying than they do about getting an MI or getting diabetes or whatever. So anyway, there's all sorts of possibilities. But we're just at the beginning of the trail of answering the questions that, and I don't even know all the questions that could be posed, but this really is very fascinating.

2:04:36And the fact that it was discovered fairly serendipitously by interrogating these epidemiologic data sets. And then the relationships seem to make sense in terms of what's been published about the detailed cell biological mechanisms, which I don't understand.

2:04:56Peter Attia:And it's an inexpensive test. It's a - Well, it's basically free if you think about it. So I mean, this is what - How many tubes of blood do you need to run it? No. So you mentioned that you got your MVX score and they probably drew an extra tube of blood for that. They don't need to do that. The same specimen, actually 150 microliters of plasma, produces the NMR spectrum that produces the NMR lipoprofile, produces glucose, produces LPIR, produces glycae, produces MVX. All of that comes from the same analysis. and when done in high volume settings, these are tests that really literally cost a dollar or less.

2:05:44Okay. But this is the problem commercially, and this is the problem with our healthcare system and the way things are set up, that there's almost a disincentive to provide analytically free information if you can't charge incrementally for it. And what you'd like to do if you're a company is charge a whole lot more for it. And then you have the tension between what you'd like to charge and what the insurance wants to pay for. And then convincing the insurance company that it's worth paying for is what keeps you from being successful commercially in producing this test globally, broadly. I had this experience with LDLP.

2:06:27Tremendous resistance to paying. So, you know, I think the way around that, one way around that is to not try to get paid incrementally for it and just do something that... So the analogy is the comprehensive metabolic panel that you get done every time you go for it. There's 14 things that are measured there. If you add up what the CMS reimbursement rate is for those 14, it comes to$60 and change. CMS pays$12 for that. And it's because these are all done at the same time. They have some clinical reason to be done at the same time. And economies of scale make it efficient enough that you could make money and people aren't going to starve producing this test getting paid$12 as much as they'd like to get paid a lot more.

2:07:18This is a situation just like that where the information is essentially free. And we made that point in the paper we wrote about the lipid panel, the extended lipid panel that includes ApoB. I mean, we didn't really get to the ApoB.

2:07:35Peter Attia:It was actually the last thing I wanted to get back to, which was the discordance between ApoB and LDLP. Let's get back to that because the ApoB story is the same as the LDLP story. And the reason that I have partnered rather than competed against Alan Snyderman, who's the biggest proponent of ApoB, is that it would be disingenuous to say that one is really better than the other. I could make the case that the NMR analysis tells you LDLP but also TRLP, triglyceride-rich particles, subspecies might be differentially related. There are people publishing papers that suggest that's true. So you could definitely be ahead of the game with more information than ApoB provides.

2:08:17ApoB is just a single measure of all the ApoB on LDL and VLDL particles. But the challenge is convincing people that you should do something other than measure cholesterol. And so you need as many people in that fight as possible. So Alan and I are both telling the same story. And that's why we transitioned. I mean, I advocated that we use NMR to produce ApoB, which actually we got FDA clearance for the quality of the ApoB information that comes from the NMR spectrum via the machine learning approach. So the idea was the extended lipid panel would have no analytic cost associated with adding ApoB to a lipid panel.

2:09:02Now you have a better lipid panel. The way that Medicare reimbursement is set up now, APOB gets paid$20, lipid panel about$13. Last I looked, it might have changed a little. So if you want to add APOB to make the lipid panel better, it's more than double the cost to the payers. The payers aren't going to want to do that. And what's the CMS reimbursement on the NMR of lipids? It's about$30 and change. And that's for the NMR lipoprofile. The NMR lipoprofile comes with LPIR. Okay. Yep. Okay. We couldn't get that FDA cleared at the time, but you might know that a lot of laboratories can offer tests that are not FDA cleared through sort of a loophole in the area where the FDA has decided to exercise discretion about whether they will enforce this or not.

2:10:04So laboratory developed tests, LDTs, individual laboratories can develop their own tests, go through a, you know, get CLIA certification. So this is more laboratory certification for the, how well they perform the test. But the actual demonstration of the clinical utility of the test is something that FDA cares about, but CLIA doesn't care about. And so it's an easier path to offering commercially a test that doesn't have to go through FDA clearance. And so LPIR was, added to the NMR lipoprofile as an LDT, not part of what was cleared. LDLP was cleared, but not without great difficulty. So anyway, a lot of the reason that NMR wasn't commercially successful has to do with what I just explained about the resistance of payers to pay any increment to what they're paying for now.

2:11:04And you really need to demonstrate. The path to getting insurance to pay is to get some advisory panels to some clinical guideline group to bless it. And Alan Snyderman can speak to the difficulty of having APOB blessed by the cholesterol guidelines. Although the European guidelines have. European guidelines and now the US guidelines are getting, but it's still, it's just ridiculous. But part of it is because the guideline writers are trying to protect the payers, which that shouldn't be their job. They should be assessing the clinical utility only and let capitalism worry about. The actual Medicare reimbursement cost for ApoB, which was set many, many years ago, has nothing to do with what it costs to do these immunoassays on these modern analyzers.

2:11:57So again, there's this complete disconnect between what's charged and what's paid for and what it costs to measure. It's the same thing that drug companies are defending the prices that they pay to support the research, et cetera. So you can make the argument that you need to stay in business, you need to make more money. But anyway, I succeeded more as a scientist than as an entrepreneur in what happened to liposcience. Because we really had gotten quite far down the road of making NMR testing broadly available to the benefit of so many people internationally. And that just got tanked when it was purchased by a lab testing company instead of an IVD company.

2:12:45So I hope anybody listening who has a few bucks -

2:12:47Peter Attia:Well, I mean, yeah, I don't know that door is closed indefinitely. No, not at all. I think that the MVX test offers a very compelling reason why another company might want to come along and purchase those assets, especially given the prognostic utility of that test. So let's talk now about this edge case of CTAP inhibition. And one of the first things that stood out to me looking at the Broadway and Brooklyn trials, which were the phase three trials of Obisetrapib, were that the reductions in LDLP and LDLC were greater than the reductions in ApoB if memory serves correctly. What do you think is happening there?

2:13:35I know what's happening. So the NMR analysis, first of all, was using an older algorithm than the one that we've been using for the last five years. But even in the older algorithm, the issue of whether NMR can reliably quantify these very abnormal HDL particles that are produced by CTIP inhibition. So HDL cholesterol doubles or more than doubles, not because the number of HDL particles doubles. In fact, the number of HDL particles actually goes down a bit overall. So what happens with CTIP inhibition is smaller particles are made into larger particles. So the number of small particles goes down, number of large particles goes up.

2:14:26These contain, the large particles contain five or 10 times more cholesterol per particle than the small ones. So HDL cholesterol goes way up and HDL particle number does not. But the problem in terms of the analysis is that there's a natural... So again, we're taking advantage of NMR signals from the different size lipoproteins being detectable and differentiated from their neighbors, right? And so at the interface of small LDL, the LDL gets so small, and then the largest HDL is its nearest neighbor. And there's a decent gap between the diameters of those particles, so they don't get confused normally.

2:15:14But when you've got C-type inhibition creating human beings that don't exist naturally and have HDL cholesterol of 120, 30, 50, your HDL particles get perilously close to the size of small LDL particles. And now NMR has the possibility of confusing the two.

2:15:33Peter Attia:So that's incredible just given the size difference between these particles normally, like the ApoB and the ApoA1 particles. I thought they were like a mile apart on that spectrum. So the good news though is that when you have metabolic situations that cause you to have large HDL, you also have the LDL size distribution skewed to the large LDL. So there's fewer or no small LDL particles. So what you can get away with in these extreme cases where somebody has really large HDL, and the NMR can tell when you encounter that situation, that you basically take away from the deconvolution model, the smallest LDL particles.

2:16:18So it doesn't have the opportunity to say this large HDL is partly small LDL. So what happened with the algorithm that was used in that study is you didn't have the opportunity to have small LDL at all, even though some small LDL was probably there. And so you saw this big decrease in LDLP,

2:16:48Peter Attia:but not ApoB because the NMR model was not allowing you. So it's really an artifact of the difficulty NMR has with this situation. So is the implication that in this case, Obisetrapib produces a disproportionate reduction in cholesterol content of particles relative to number of particles. So I know you've talked about Obisetrapib, and you and most people are very optimistic about the prospects of Obisetrapib, despite CETP inhibition not panning out for many other drugs. And of course, these were all initially investigated because of the potential to create higher HDL cholesterol. And now we certainly know that HDL cholesterol is not the HDL biomarker of interest, and people were being fooled into thinking that would have benefit.

2:17:45Part of the reason I think, and this is pure speculation, but it comes from somewhere, that even in the face of the CETP inhibitors that came closest to being efficacious, 20%, 30 % LDL reduction, but no benefit. Maybe something bad was happening on the HDL side to counteract what was good happening on the LDL side. What was bad on the HDL side, given the understanding now that just having a lot of large cholesterol-rich HDL particles It doesn't put you ahead of the game in terms of cardiovascular risk. What we now know is that small HDLP is powerfully related to mortality, all-cause mortality.

2:18:29So what I told you is true, especially with the most powerful CETP inhibitors. They reduce small HDLP by 10 or 20%. If you ignore what's going on in HDL and you only look at what's happening with ApoB and LDL, You think obocetrapid is a no-brainer. It's going to be positive. But what if people are actually being hurt, maybe in terms of mortality risk, by the small HDLP going down if there is a causal relationship there? Don't know that there is yet. We haven't proved that. There's biological plausibility because of the proteins that hang on to small HDL particles, which is partly why we think it makes sense in terms of anti-oxidation, anti-inflammation, that small HDL particles might have this inverse association with mortality risk.

2:19:19So anyway.

2:19:20Peter Attia:But are you saying that you think that it's possible that, because again, we still don't have the hard outcome trial, but your thinking is that if the hard outcome trial demonstrates utility, it might be, you're saying it could be just due to the reduction in small HDLP more than I'm suggesting that people's predictions about how much efficacy there's going to be could be wrong if they're based on the LDL reduction. It may still be that the trial overall is positive or positive enough to have the drug go forward. But the most dramatic demonstration of something bad happening while something good is happening and the two counteracting each other is if the trial doesn't succeed like the other ones succeeded.

2:20:07So we have to wait for the trial. And then if the trial doesn't succeed, then I'll say, yay, I was right. But it's pure speculation. Yeah.

2:20:19Peter Attia:Yeah. Well, very interesting. And I'll have to go back and look and see what the magnitude of the ApoB reduction was. But I just remember that it was less than - It was less, but it's still - But it's still reduced. Yeah, significant. But you're heartened by the fact that small HDLP was decreased. Yeah. And we've actually reanalyzed that data set with the more recent. We do a better job in differentiating large HDL from small LDL. And so those LDLP results are much more in line with the ApoB reductions than that paper indicated. Well, Jim, this is such a fascinating space. This is a discussion that has been long overdue.

2:21:03Peter Attia:Again, I don't think there's many people listening to us that haven't at least heard of LDL-P, HDL-P. They might not know what liposcience is. They might not understand in vitro diagnostics and any of the other things that go around to it. Probably a lot of people are not familiar with MVX, but my hope is that that starts to change after this. So regardless of how you think you've fared as an entrepreneur, you've, you've, you've, you've fared remarkably well as a scientist. And I think that's the most important thing because without the scientific foundation, I don't think any of the entrepreneurial stuff matters, but we do typically want them to be aligned.

2:21:44Peter Attia:But I, but, but put it this way at the risk of, uh, insulting an entrepreneur, I would argue that it's easier to find a good entrepreneur that it is to find a good scientist. Yeah, that's true. And that's sort of the frustration that the science is so solid, so much more solid than many startup companies are investing in. But at the end of the game, it's commercial. It's financial. I suspect it's the sector, right? I suspect you wouldn't have this difficulty getting people interested if we were talking about therapeutics. I just think that the diagnostic space and the reimbursement environment in the United States is one that is not especially attractive to investors.

2:22:31Peter Attia:That's my suspicion as to the issue. And that's why the MVX, I think, offers more than just a diagnostic. You know, if it could be paired to a therapy, if it has the ability to save enormous cost on the back end with respect to therapeutic selections, you know, there are a few trials that need to be done to demonstrate that. But to me, that's the interesting area. No, you're right. And that's what will make it successful commercially. My vision, though, that wasn't realized is how cool would it be to go to your yearly physical and get a lipid panel that had glucose, LPIR, glyc -A, MVX, at no incremental cost.

2:23:17In the rest of the world, not the U.S., where you have national health care, there is a premium put on how efficient a diagnostic is. And if you can get a lot of information for less work and money, that's worth something in the rest of the world. It's just, you know, we tried to skin that cat in the U.S. So it's, you know, I haven't lost hope, but I really left LabCorp because I didn't want to beat my head against that wall any longer and wanted to spend my remaining years doing the science. So that's what I'm continuing to do.

2:23:52Peter Attia:Fantastic. Well, thank you, Jim. And thanks for taking the time to come out here today. You bet. Thank you for listening to this week's episode of The Drive. Head over to peteratiamd.com forward slash show notes if you want to dig deeper into this episode. You can also find me on YouTube, Instagram, and Twitter, all with the handle peteratiamd. You can also leave us a review on Apple Podcasts or whatever podcast player you use. This podcast is for general informational purposes only and does not constitute the practice of medicine, nursing, or other professional healthcare services, including the giving of medical advice.

2:24:29Peter Attia:No doctor-patient relationship is formed. The use of this information and the materials linked to this podcast is at the user's own risk. The content on this podcast is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Users should not disregard or delay in obtaining medical advice from any medical condition they have, and they should seek the assistance of their healthcare professionals for any such conditions. Finally, I take all conflicts of interest very seriously. For all of my disclosures and the companies I invest in or advise, please visit peteratiamd.com forward slash about where I keep an up-to-date and active list of all disclosures.

2:25:13We'll see you next time.